An apparatus and method for generating a process enhancement, the apparatus comprising a memory and a processor configured to receive process data, receive user input, determine a plurality of response modules as a function of the user input, determine a modification target as function of the plurality of response modules, wherein determining the modification target includes calculating an importance score for each response module of the plurality of response modules, ranking each response module of the plurality of response modules as a function of the importance score and determining the modification target as a function of the ranking, identify at least a process modification as a function of the process data and the modification target and generate the process enhancement as a function of the at least a process modification.
Legal claims defining the scope of protection, as filed with the USPTO.
2. The apparatus of claim 1, wherein receiving user input comprises receiving the user input using a chatbot.
3. The apparatus of claim 1, wherein the processor is further configured to generate enhancement training data.
5. The apparatus of claim 4, wherein the processor is further configured to add the additional process modification to the module training data.
6. The apparatus of claim 1, wherein calculating the importance score comprises maximizing an objective function.
7. The apparatus of claim 1, wherein the response module machine learning model is a neural network.
8. The apparatus of claim 1, wherein the processor is further configured to add the process enhancement to the process data.
9. The apparatus of claim 1, wherein the processor is configured to calculate the importance score as a function of a priority.
10. The apparatus of claim 1, wherein determining the modification target comprises using a fuzzy set comparison.
12. The method of claim 11, wherein receiving the user input comprises using a chatbot.
13. The method of claim 11, wherein the method further comprises generating enhancement training data.
15. The method of claim 14, wherein the method further comprises adding the additional process modification to the module training data.
16. The method of claim 11, wherein calculating the importance score comprises maximizing an objective function.
17. The method of claim 11, wherein the response module machine learning model is a neural network.
18. The method of claim 11, wherein the method further comprises adding the process enhancement to the process data.
19. The method of claim 11, wherein the method further comprises calculating the importance score as a function of a priority.
20. The method of claim 11, wherein determining the modification target comprises using a fuzzy set comparison.
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May 26, 2023
February 20, 2024
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